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arm32 winograd init optimize

This commit is contained in:
liuzhongkai 2020-09-30 11:51:15 +08:00
parent 4f754daccf
commit 8f678accf3
3 changed files with 180 additions and 4 deletions
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176
mindspore/lite/nnacl/assembly/arm32/MatmulWinogradFp32.S Normal file
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@ -0,0 +1,176 @@
#ifdef ENABLE_ARM32
.text
.align 5
.global MatrixMultiplyWinograd
#ifndef __APPLE__
.type MatrixMultiplyWinograd, %function
#endif
// MatrixMultiplyWinograd(float *matix_a, float *matrix_b, float *matrix_c, int m, int k, int n, int in_channel, int c4_channel)
// r0: matrix_a, r1: matrix_b, r2: matrix_c, r3: m, r4: k, r5: n, r6: in_channel, r7: c4_channel * 4
// #-56: matrix_a, #-52: matrix_b, #-48: matrix_c, #-44: m, #0: k, #4: n, #8: in_channel, #12: c4_channel * 4
MatrixMultiplyWinograd:
// at return, clang generates "push {lr}, pop {pc}"" while gcc will generate "bx lr"
// according to https://stackoverflow.com/questions/53625807
// even if we jump to link register instead of saving it, we still have to save it in subroutine calls anyway
// clang's rule seems more simple, though there are no subroutine calls here
// r4-r8 and q4-q7 must be saved according to https://static.docs.arm.com/ihi0042/i/aapcs32.pdf
push {r0-r12, lr}
vpush {q4-q7}
add sp, sp, #120
mov r0, #4
ldr r4, [sp, #4] // n
ldr r5, [sp, #8] // in_channel
ldr r6, [sp] // k
mul r5, r5, r0 // in_channel * 4
mul r4, r4, r0 // n * 4
mul r6, r6, r5 // in_channel * 4 * k
// r3 = m
// r2 = dst
LoopM:
ldr r7, [sp, #4] // n
ldr r8, [sp, #-52] // matrix_b
LoopN:
ldr r0, [sp, #4] // n
ldr r1, [sp, #-44] // m
sub r0, r0, r7 // ni
mul r0, r0, r1 // ni * m
sub r1, r1, r3 // mi
add r0, r0, r1 // ni * m + mi
ldr r1, [sp, #12]
mul r9, r0, r1 // (ni * m + mi) * c4_channel * 4
add r11, r2, r9 // dst + offset
ldr r10, [sp, #8] // in_channel
ldr r9, [sp, #-56] // src
cmp r10, #16
bge LoopC16
cmp r10, #8
bge LoopC8
cmp r10, #4
bge LoopC4
cmp r10, #1
bge LoopC
b EndLoopC
LoopC16:
mov r0, r8 // mat_b1
ldr r12, [sp] // k
veor q5, q5, q5
veor q6, q6, q6
veor q7, q7, q7
veor q8, q8, q8
LoopK16:
vld1.32 {q0, q1}, [r9]!
vld1.32 {q2, q3}, [r9]!
add r9, r9, r5
sub r9, r9, #64
vld1.32 d8[0], [r0], r4
vmla.f32 q5, q0, d8[0]
vmla.f32 q6, q1, d8[0]
vmla.f32 q7, q2, d8[0]
vmla.f32 q8, q3, d8[0]
subs r12, r12, #1
bne LoopK16
Write16:
vst1.32 {q5, q6}, [r11]!
vst1.32 {q7, q8}, [r11]!
subs r10, r10, #16
beq EndLoopC
sub r9, r9, r6
add r9, r9, #64
cmp r10, #16
bge LoopC16
cmp r10, #8
bge LoopC8
cmp r10, #4
bge LoopC4
cmp r10, #1
bge LoopC
LoopC8:
veor q5, q5, q5
veor q6, q6, q6
mov r0, r8 // mat_b1
ldr r12, [sp] // k
LoopK8:
vld1.32 {q0, q1}, [r9], r5
vld1.32 d8[0], [r0], r4
vmla.f32 q5, q0, d8[0]
vmla.f32 q6, q1, d8[0]
subs r12, r12, #1
bne LoopK8
Write8:
vst1.32 {q5, q6}, [r11]!
subs r10, r10, #8
beq EndLoopC
sub r9, r9, r6
add r9, r9, #32
cmp r10, #8
bge LoopC8
cmp r10, #4
bge LoopC4
cmp r10, #1
bge LoopC
LoopC4:
veor q5, q5, q5
mov r0, r8 // mat_b1
ldr r12, [sp] // k
LoopK4:
vld1.32 {q0}, [r9], r5
vld1.32 d8[0], [r0], r4
vmla.f32 q5, q0, d8[0]
subs r12, r12, #1
bne LoopK4
Write4:
vst1.32 {q5}, [r11]!
subs r10, r10, #4
beq EndLoopC
sub r9, r9, r6
add r9, r9, #16
cmp r10, #4
bge LoopC4
cmp r10, #1
bge LoopC
LoopC:
veor q2, q2, q2
mov r0, r8 // mat_b1
ldr r12, [sp] // k
LoopK:
vld1.32 {s0}, [r9], r5
vld1.32 d2[0], [r0], r4
vmla.f32 s8, s0, s4
subs r12, r12, #1
bne LoopK
Write:
vst1.32 d4[0], [r11]!
subs r10, r10, #1
beq EndLoopC
sub r9, r9, r6
add r9, r9, #4
b LoopC
EndLoopC:
subs r7, r7, #1
beq EndLoopN
add r8, r8, #4
b LoopN
EndLoopN:
subs r3, r3, #1
beq EndLoopM
ldr r0, [sp, #-56]
add r0, r0, r6
str r0, [sp, #-56]
b LoopM
EndLoopM:
sub sp, sp, #120
vpop {q4-q7}
pop {r0-r12, pc}
#endif

2
mindspore/lite/nnacl/minimal_filtering_generator.c
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@ -121,7 +121,7 @@ int B(float *poly_array, float *matrix_b, int in_unit) {
return NNACL_OK;
}
#ifndef ENABLE_ARM64
#ifndef ENABLE_ARM
void MatrixMultiplyWinograd(const float *matix_a, const float *matrix_b, float *matrix_c, int m, int k, int n,
int in_channel, int c4_channel) {
int cnt = 0;

6
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_winograd.cc
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@ -57,7 +57,7 @@ int ConvolutionWinogradCPUKernel::WinogradFilterTransform(const float *weight_da
return RET_MEMORY_FAILED;
}
#ifndef ENABLE_ARM64
#ifndef ENABLE_ARM
auto tmp_data1 = reinterpret_cast<float *>(malloc(channel_in * input_unit_ * kernel_unit_ * sizeof(float)));
if (tmp_data1 == nullptr) {
free(tmp_data);
@ -81,7 +81,7 @@ int ConvolutionWinogradCPUKernel::WinogradFilterTransform(const float *weight_da
int out_c_res = i % oc_block;
int output_oz_offset = out_c_block * block_stride + out_c_res;
#ifndef ENABLE_ARM64
#ifndef ENABLE_ARM
// tmp_data = g * GT
MatrixMultiplyWinograd(weight_data + i * input_oz_offset, matrix_gt, tmp_data, kernel_unit_, kernel_unit_,
input_unit_, channel_in, channel_in * 4);
@ -112,7 +112,7 @@ int ConvolutionWinogradCPUKernel::WinogradFilterTransform(const float *weight_da
}
}
}
#ifndef ENABLE_ARM64
#ifndef ENABLE_ARM
free(tmp_data1);
free(trans_out_data1);
#endif